Wind power plant scheduling method and system based on fatigue distribution

Abstract

The invention relates to the technical field of wind power plants, in particular to a wind power plant scheduling method and system based on fatigue distribution. Comprising the steps of establishing a plurality of fan points according to wind turbine generator parameters; establishing a plurality of adjustment periods according to the historical environment parameters, and generating expected power of each fan point in the current adjustment period according to a preset power prediction model; generating an initial scheduling strategy according to the power grid scheduling instruction in the current adjustment period and the expected power of all the fan points; obtaining a fatigue evaluation value of each fan point according to a preset feedback time node, and judging whether to correct the initial scheduling strategy or not according to all the fatigue evaluation values; and generating an initial scheduling strategy according to the operation parameters of each wind turbine generator, and performing optimization adjustment on the initial scheduling strategy according to all fatigue evaluation values, thereby optimizing the uniformity of fatigue distribution of the wind power plant, prolonging the service life of the wind power plant and reducing the operation and maintenance cost of the wind power plant on the premise of not losing the generating capacity.

Description

Technical Field

[0001] The present application relates to the technical field of wind farms, and in particular to a wind farm scheduling method and system based on fatigue distribution. Background Art

[0002] Most of my country's inland areas are mountainous and hilly. The elevation difference at the installation location of the wind turbine is large, and the change in elevation will affect the change in wind speed. Coupled with the influence of wake vortex, the wind speed input by each unit is seriously uneven, resulting in uncertainty in the load it bears.

[0003] At present, the control strategies for active power of wind farms generally focus on whether the output power can meet the needs of the power grid dispatching center, thus ignoring the operation of the wind farm itself. This increases the operating burden of the unit, ultimately leading to an increase in failure rate, a decrease in service life, and a deterioration in the operating economy of the entire wind farm. Summary of the invention

[0004] The purpose of this application is: to solve the above technical problems, this application provides a wind farm scheduling method and system based on fatigue distribution, aiming to improve the operating efficiency and operating income of wind turbines, extend the service life of the units, and reduce the operation and maintenance costs of wind farms.

[0005] In some embodiments of the present application, an initial scheduling strategy is generated based on the operating parameters of each wind turbine set, and a fatigue evaluation value of each wind turbine set is generated based on a preset feedback time node. The initial scheduling strategy is optimized and adjusted based on all fatigue evaluation values. Without losing power generation, the uniformity of fatigue distribution in the wind farm is optimized, the service life of the unit is extended, and the operation and maintenance cost of the wind farm is reduced.

[0006] In some embodiments of the present application, by periodically monitoring the uniformity of fatigue distribution in the wind farm, the operating parameters of the excessively worn wind turbines are adjusted in time, and the fatigue characteristics of the wind turbines are improved by controlling the speed and pitch angle of the wind turbines or shutting down for maintenance, so as to avoid a decrease in the operating efficiency and shortened operating life of the wind turbines due to excessive wear.

[0007] In some embodiments of the present application, a wind farm scheduling method based on fatigue distribution is provided, including: Establish multiple wind turbine points according to wind turbine parameters; Establish multiple regulation cycles based on historical environmental parameters, and generate the expected power of each wind turbine point in the current regulation cycle based on the preset power prediction model; Generate an initial dispatching strategy based on the grid dispatching instructions in the current regulation cycle and the expected power of all wind turbines; Obtain the fatigue evaluation value of each wind turbine point according to the preset feedback time node, and determine whether to modify the initial scheduling strategy based on all fatigue evaluation values; When multiple wind turbine points are established, it includes: Generate fan point number sequence A, A=(a1,a2…a i …a n ), where a i is the i-th wind turbine point, and n is the number of wind turbine points.

[0008] In some embodiments of the present application, when generating an initial scheduling strategy, the process includes: Establish the expected power series P in the current regulation cycle, P=(p1,p2…p i …p n ), where pi is the expected power of the i-th wind turbine point in the current regulation cycle; Generates power demand b' according to the grid dispatch instruction; Generate the primary power of each wind turbine point in the current regulation cycle according to the expected power series P and the required power b'; Establish a first-level power series B, B=(b1, b2…b i …b n ),b i is the primary power of the i-th fan point in the current regulation cycle; Among them, b i =b'*[p i / ( p i )]; Generate an initial scheduling strategy for the current regulation period according to the first-level power series B.

[0009] In some embodiments of the present application, when determining whether to modify the initial scheduling strategy according to all fatigue evaluation values, it includes: Set multiple feedback time nodes within the current adjustment cycle; Obtain the fatigue evaluation value of each fan point at the current feedback time node; Establish fatigue evaluation value series C, C=(c1,c2…c i …c n ), where c i is the fatigue evaluation value of the i-th wind turbine at the current feedback time node; Generate a corrected evaluation value g of the current feedback time node according to the fatigue evaluation value sequence C; Whether or not to generate a correction instruction is determined based on the correction evaluation value g.

[0010] In some embodiments of the present application, when generating a modified evaluation value g of the current feedback time node, it includes: g=e1*Q1* (c i -c') 2 +e2*Q2* (c i -∆c) 2 +e3*Q3*U; U= Y(i)*(c i -c'); Among them, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; e3 is a preset third weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; Q3 is a preset third fixed coefficient; c' is the expected fatigue evaluation value at the current feedback time node; ∆c is the average value of all data in the fatigue evaluation value sequence C; Y(i) is a selection coefficient. If (c i -c')>0, Y(i)=1 / (c i -c'); if (c i -c')<0, Y(i)=0.

[0011] In some embodiments of the present application, when judging whether to generate a correction instruction according to the corrected evaluation value g, it includes: Presetting a first corrected evaluation value threshold G1 and a second corrected evaluation value threshold G2; If g < G1, no correction instruction is generated; If G1 ≤ g < G2, a first-level correction instruction is generated, and a power compensation coefficient for each fan point is generated according to the first-level correction instruction; The initial scheduling strategy is corrected according to all the power compensation coefficients; If g ≥ G2, a second-level correction instruction is generated, and the initial scheduling strategy is corrected according to the second-level correction instruction.

[0012] In some embodiments of the present application, when correcting the initial scheduling strategy according to the second-level correction instruction, it includes: Generating a fatigue evaluation value threshold C1 at the current feedback time node; If c i > C1, then the i-th fan point is excluded, and the i-th fan point is set as a fan point to be overhauled; An overhaul plan is generated according to all the fan points to be overhauled; According to the exclusion result, a first-level fan point sequence A1 at the current feedback time node is established; A1=(a 11 ,a 12 …a 1i …a 1n1 ), where is the i-th unexcluded fan point at the current feedback time node; n1 is the number of unexcluded fan points at the current feedback time node, and n1 ≤ n; Generate the allocation coefficient of each primary fan point according to the expected power and fatigue evaluation value of each primary fan point; The first-level scheduling strategy of the current feedback time node is generated according to the allocation coefficient and the required power b'.

[0013] In some embodiments of the present application, a wind farm dispatching system based on fatigue distribution is provided, including: The central control unit is used to establish multiple wind turbine points according to the wind turbine parameters; A monitoring unit, comprising a plurality of monitoring submodules, wherein the monitoring submodules are arranged at the fan points and are used to collect operating parameters of each fan point; The central control unit comprises: A first processing module, used to establish a plurality of adjustment cycles according to historical environmental parameters, and the first processing module is also used to generate the expected power of each wind turbine point in the current adjustment cycle according to a preset power prediction model; The second processing module is used to generate an initial dispatching strategy according to the grid dispatching instructions and the expected power of all wind turbine points in the current regulation cycle; The third processing module is used to obtain the fatigue evaluation value of each wind turbine point according to the preset feedback time node; A correction module, used to determine whether to correct the initial scheduling strategy according to all fatigue evaluation values; The fourth processing module is used to generate a fan point number sequence A, A=(a1, a2…a i …a n ), where a i is the i-th fan point, n is the number of fan points; The second processing module is also used for: Establish the expected power series P in the current regulation cycle, P=(p1,p2…p i …p n ), where pi is the expected power of the i-th wind turbine point in the current regulation cycle; Generates power demand b' according to the grid dispatch instruction; Generate the primary power of each wind turbine point in the current regulation cycle according to the expected power series P and the required power b'; Establish a first-level power series B, B=(b1, b2…b i …b n ),b i is the primary power of the i-th fan point in the current regulation cycle; Among them, b i =b'*[p i / ( p i )]; Generate the initial scheduling strategy for the current adjustment period according to the first-level power sequence B.

[0014] In some embodiments of the present application, the third processing module is further configured to: Set multiple feedback time nodes within the current adjustment period; Obtain the fatigue evaluation values of each fan at the current feedback time node; Establish a fatigue evaluation value sequence C, C = (c1, c2…c i …c n ), where c i is the fatigue evaluation value of the i-th fan at the current feedback time node.

[0015] In some embodiments of the present application, the correction module is further configured to: Generate a correction evaluation value g for the current feedback time node according to the fatigue evaluation value sequence C; Judge whether to generate a correction instruction according to the correction evaluation value g.

[0016] g = e1 * Q1 * (c i - c') 2 + e2 * Q2 * (c i - ∆c) 2 + e3 * Q3 * U; U = Y(i) * (c i - c'); Wherein, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; e3 is a preset third weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; Q3 is a preset third fixed coefficient; c' is the expected fatigue evaluation value at the current feedback time node; ∆c is the average value of all data in the fatigue evaluation value sequence C; Y(i) is a selection coefficient. If (c i - c') > 0, Y(i) = 1 / (c i - c'); if (c i - c') < 0, Y(i) = 0; Judge whether to generate a correction instruction according to the correction evaluation value g.

[0017] In some embodiments of the present application, when judging whether to generate a correction instruction according to the correction evaluation value g, it includes: Preset a first correction evaluation value threshold G1 and a second correction evaluation value threshold G2; If g < G1, no correction instruction is generated; If G1 ≤ g < G2, generate a first-level correction instruction, and generate a power compensation coefficient for each fan point according to the first-level correction instruction; Modify the initial scheduling strategy according to the total power compensation coefficient; If g≥G2, a secondary correction instruction is generated, and the initial scheduling strategy is corrected according to the secondary correction instruction.

[0018] Compared with the prior art, the wind farm scheduling method and system based on fatigue distribution in the embodiment of the present application has the following beneficial effects: An initial dispatching strategy is generated based on the operating parameters of each wind turbine set, and a fatigue evaluation value of each wind turbine set is generated based on the preset feedback time node. The initial dispatching strategy is optimized and adjusted based on all fatigue evaluation values. Without losing power generation, the uniformity of fatigue distribution in the wind farm is optimized, the service life of the unit is extended, and the operation and maintenance cost of the wind farm is reduced.

[0019] By periodically monitoring the uniformity of fatigue distribution in wind farms, the operating parameters of excessively worn wind turbines can be adjusted in a timely manner. The fatigue characteristics of wind turbines can be improved by controlling the speed and pitch angle of the turbines or shutting them down for maintenance, thus avoiding a decrease in wind turbine operating efficiency and a reduction in operating life due to excessive wear. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a wind farm scheduling method based on fatigue distribution in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0021] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0022] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0023] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0024] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0025] like Figure 1 As shown, the preferred embodiment of the present application is a wind farm scheduling method based on fatigue distribution, comprising: S101: Establish multiple wind turbine points according to wind turbine parameters; S102: establishing multiple adjustment cycles according to historical environmental parameters, and generating expected power of each wind turbine point in the current adjustment cycle according to a preset power prediction model; S103: generating an initial dispatching strategy according to the grid dispatching instructions in the current regulation cycle and the expected power of all wind turbine points; S104: Obtain fatigue evaluation values ​​of each wind turbine point according to a preset feedback time node, and determine whether to modify the initial scheduling strategy according to all fatigue evaluation values; When multiple wind turbine points are established, it includes: Generate fan point number sequence A, A=(a1,a2…a i …a n ), where a i is the i-th wind turbine point, and n is the number of wind turbine points.

[0026] Specifically, a single wind turbine point represents a wind turbine. The duration of a single regulation cycle is set according to the historical demand fluctuation parameters and historical environmental fluctuation parameters of the power grid. The more severe the fluctuation, the shorter the corresponding regulation cycle, thereby ensuring the accuracy of the predicted value of the power prediction model within a single regulation cycle and improving the dispatch efficiency of all wind turbines in the wind farm.

[0027] Specifically, when generating the initial scheduling strategy, it includes: Establish the expected power series P in the current regulation cycle, P=(p1,p2…p i …p n ), where pi is the expected power of the i-th wind turbine point in the current regulation cycle; Generates power demand b' according to the grid dispatch instruction; Generate the primary power of each wind turbine point in the current regulation cycle according to the expected power series P and the required power b'; Establish a first-level power series B, B=(b1, b2…b i …bn ),b i is the primary power of the i-th fan point in the current regulation cycle; Among them, b i =b'*[p i / ( p i )]; Generate an initial scheduling strategy for the current regulation period according to the first-level power series B.

[0028] Specifically, the expected power refers to the maximum active power that the corresponding wind turbine can provide in the current regulation cycle, and the corresponding initial scheduling strategy is generated based on the average principle.

[0029] It can be understood that in the above embodiment, an initial scheduling strategy is generated according to the operating parameters of each wind turbine set, and a fatigue evaluation value of each wind turbine set is generated according to a preset feedback time node. The initial scheduling strategy is optimized and adjusted according to all fatigue evaluation values. Without losing power generation, the uniformity of fatigue distribution of the wind farm is optimized, the service life of the unit is extended, and the operation and maintenance cost of the wind farm is reduced.

[0030] In a preferred embodiment of the present application, when judging whether to modify the initial scheduling strategy according to all fatigue evaluation values, it includes: Set multiple feedback time nodes within the current adjustment cycle; Obtain the fatigue evaluation value of each fan point at the current feedback time node; Establish fatigue evaluation value series C, C=(c1,c2…c i …c n ), where c i is the fatigue evaluation value of the i-th wind turbine at the current feedback time node; Generate a corrected evaluation value g of the current feedback time node according to the fatigue evaluation value sequence C; Whether or not to generate a correction instruction is determined based on the correction evaluation value g.

[0031] Specifically, the fatigue load of the wind turbine is concentrated on the transmission system load caused by shaft torsion and the tower structure load caused by tower deflection. The fatigue load of the wind turbine can be judged by parameters such as shaft torque and tower thrust, thereby generating a corresponding fatigue evaluation value. The larger the fatigue evaluation value, the greater the fatigue degree of the current wind turbine.

[0032] Specifically, by establishing multiple feedback time nodes and periodically monitoring the fatigue status of each wind turbine, the initial scheduling strategy can be optimized and corrected to improve the uniformity of fatigue distribution within the wind farm.

[0033] Specifically, when generating the modified evaluation value g of the current feedback time node, it includes: g = e1 * Q1 * (c i - c') 2 + e2 * Q2 * (c i - ∆c) 2 + e3 * Q3 * U; U = Y(i) * (c i - c'); Wherein, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; e3 is a preset third weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; Q3 is a preset third fixed coefficient; c' is the expected fatigue evaluation value at the current feedback time node; ∆c is the average value of all data in the fatigue evaluation value sequence C; Y(i) is a selection coefficient. If (c i - c') > 0, Y(i) = 1 / (c i - c'); if (c i - c') < 0, Y(i) = 0.

[0034] Specifically, all parameters in the model are normalized by presetting the first fixed coefficient, the second fixed coefficient, and the third fixed coefficient, so that each parameter is within the same value range.

[0035] Specifically, the expected fatigue evaluation value at the current feedback time node is generated according to historical parameters, and the expected fatigue evaluation value refers to the fatigue loss of the wind turbine when it operates in an ideal state and accumulates to the current feedback time node as the operation time increases.

[0036] Specifically, the larger the correction evaluation value is, the worse the uniformity of the fatigue distribution in the current wind farm is, and the more wind turbines with fatigue levels greater than expected there are. It is necessary to correct and optimize the current initial scheduling strategy in a timely manner.

[0037] It can be understood that in the above embodiments, by periodically monitoring the uniformity of the fatigue distribution in the wind farm, the operating parameters of the overly worn wind turbines are adjusted in a timely manner. By controlling the turbine speed, pitch angle, or shutdown maintenance, etc., the fatigue characteristics of the wind turbines are improved, and the decrease in the operating efficiency and the reduction of the operating life of the wind turbines caused by excessive wear are avoided.

[0038] In the preferred embodiment of the present application, when judging whether to generate a correction instruction according to the correction evaluation value g, it includes: Presetting a first correction evaluation value threshold G1 and a second correction evaluation value threshold G2; If g < G1, no correction instruction is generated; If G1 ≤ g < G2, generate a first-level correction instruction, and generate a power compensation coefficient for each wind turbine point according to the first-level correction instruction; Correct the initial scheduling strategy according to all the power compensation coefficients; If g ≥ G2, generate a second-level correction instruction, and correct the initial scheduling strategy according to the second-level correction instruction.

[0039] Specifically, the first correction evaluation value threshold and the second correction evaluation value threshold can be set according to historical parameters.

[0040] Specifically, when generating the first-level correction instruction, it indicates that the uniformity of the fatigue distribution in the current wind farm is poor. The superior scheduling instruction is proportionally allocated according to the output capacity and fatigue degree of each wind turbine unit, and through optimization processing, a power compensation coefficient for each wind turbine unit is generated, so as to realize the scheduling optimization of each wind turbine unit, optimize the uniformity of the fatigue distribution in the wind farm without losing power generation, extend the service life of the units, and reduce the operation and maintenance costs of the wind farm.

[0041] Specifically, when correcting the initial scheduling strategy according to the second-level correction instruction, it includes: Generate the fatigue evaluation value threshold C1 of the current feedback time node; If c i > C1, then eliminate the i-th wind turbine point, and set the i-th wind turbine point as the wind turbine point to be overhauled; Generate an overhaul plan according to all the wind turbine points to be overhauled; Establish a first-level wind turbine point sequence A1 of the current feedback time node according to the elimination result; A1 = (a 11 , a 12 … a 1i … a 1n1 ), where is the i-th uneliminated wind turbine point of the current feedback time node; n1 is the number of uneliminated wind turbine points of the current feedback time node, and n1 ≤ n; Generate an allocation coefficient for each first-level wind turbine point according to the expected power and fatigue evaluation value of each first-level wind turbine point; Generate a first-level scheduling strategy of the current feedback time node according to the allocation coefficient and the required power b'.

[0042] Specifically, the fatigue evaluation value threshold can be set according to historical parameters. When c i > C1, it indicates that there is abnormal loss in the i-th wind turbine unit, and it needs to be overhauled in time to improve the operation life of the wind turbine unit.

[0043] Specifically, by shutting down some wind turbines with abnormal losses, their losses can be reduced and the operating life of wind turbines can be improved. At the same time, the superior dispatching instructions are proportionally distributed to the remaining first-level wind turbines according to their output capacity and fatigue level, and a first-level dispatching strategy is generated after optimization processing.

[0044] Specifically, if the total expected power of the remaining first-level wind turbine points is lower than the required power b', each first-level wind turbine point generates electricity according to the expected power, and the operating parameters of each eliminated wind turbine point are set according to the power difference between the total expected power and the required power b'.

[0045] Based on another preferred embodiment of a wind farm scheduling method based on fatigue distribution in any of the above preferred embodiments, this preferred embodiment provides a wind farm scheduling method based on fatigue distribution, including: The central control unit is used to establish multiple wind turbine points according to the wind turbine parameters; A monitoring unit, including a plurality of monitoring submodules, which are arranged at the fan points and used to collect the operating parameters of each fan point; The central control unit includes: A first processing module, used to establish multiple adjustment cycles according to historical environmental parameters, and the first processing module is also used to generate the expected power of each wind turbine point in the current adjustment cycle according to a preset power prediction model; The second processing module is used to generate an initial dispatching strategy according to the grid dispatching instructions and the expected power of all wind turbine points in the current regulation cycle; The third processing module is used to obtain the fatigue evaluation value of each wind turbine point according to the preset feedback time node; A correction module, used to determine whether to correct the initial scheduling strategy according to all fatigue evaluation values; The fourth processing module is used to generate a fan point number sequence A, A=(a1, a2…a i …a n ), where a i is the i-th fan point, n is the number of fan points; The second processing module is also used for: Establish the expected power series P in the current regulation cycle, P=(p1,p2…p i …p n ), where pi is the expected power of the i-th wind turbine point in the current regulation cycle; Generates power demand b' according to the grid dispatch instruction; Generate the primary power of each wind turbine point in the current regulation cycle according to the expected power series P and the required power b'; Establish a first-level power series B, B=(b1, b2…b i …bn ),b i is the primary power of the i-th fan point in the current regulation cycle; Among them, b i =b'*[p i / ( p i )]; Generate an initial scheduling strategy for the current regulation period according to the first-level power series B.

[0046] Specifically, the third processing module is also used for: Set multiple feedback time nodes within the current adjustment cycle; Obtain the fatigue evaluation value of each fan point at the current feedback time node; Establish fatigue evaluation value series C, C=(c1,c2…c i …c n ), where c i is the fatigue evaluation value of the i-th wind turbine at the current feedback time node.

[0047] In a preferred embodiment of the present application, the correction module is also used to: Generate a corrected evaluation value g of the current feedback time node according to the fatigue evaluation value sequence C; Whether or not to generate a correction instruction is determined based on the correction evaluation value g.

[0048] g=e1*Q1* (c i -c') 2 ]+e2*Q2* (c i -∆c) 2 ]+e3*Q3*U; U= Y(i)*(c i -c'); Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; c' is the expected fatigue evaluation value of the current feedback time node; ∆c is the average value of all data in the fatigue evaluation value series C; Y(i) is the selection coefficient, if (c i -c')>0,Y(i)=1 / (c i -c'); if (c i -c')<0,Y(i)=0; Whether or not to generate a correction instruction is determined based on the correction evaluation value g.

[0049] Specifically, when determining whether to generate a correction instruction according to the correction evaluation value g, it includes: Preset a first corrected evaluation value threshold G1 and a second corrected evaluation value threshold G2; If g < G1, no correction instruction is generated; If G1 ≤ g < G2, a first-level correction instruction is generated, and a power compensation coefficient for each wind turbine point is generated according to the first-level correction instruction; Correct the initial scheduling strategy according to all the power compensation coefficients; If g ≥ G2, a second-level correction instruction is generated, and the initial scheduling strategy is corrected according to the second-level correction instruction.

[0050] According to the first concept of the present application, an initial scheduling strategy is generated based on the operating parameters of each wind turbine generator, and a fatigue evaluation value for each wind turbine generator is generated according to a preset feedback time node. The initial scheduling strategy is optimized and adjusted according to all the fatigue evaluation values, so as to optimize the uniformity of the fatigue distribution of the wind farm, extend the service life of the units, and reduce the operation and maintenance costs of the wind farm without sacrificing power generation.

[0051] According to the second concept of the present application, by periodically monitoring the uniformity of the fatigue distribution of the wind farm, the operating parameters of the wind turbine generators with excessive wear are adjusted in a timely manner, and the fatigue characteristics of the wind turbine generators are improved by controlling the unit speed, pitch angle or shutdown maintenance, etc., so as to avoid the decline of the operating efficiency and the reduction of the operating life of the wind turbine generators caused by excessive wear.

[0052] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art in the technical field, without departing from the technical principle of the present application, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application.

Claims

1. A wind farm dispatching method based on fatigue distribution, characterized in that: Including: Establishing multiple wind turbine points according to the wind turbine parameters; Establishing multiple adjustment cycles according to the historical environmental parameters, and generating the expected power of each wind turbine point within the current adjustment cycle according to the preset power prediction model; Generating an initial scheduling strategy according to the grid dispatching instruction within the current adjustment cycle and the expected power of all wind turbine points; Obtaining the fatigue evaluation value of each wind turbine point according to the preset feedback time node, and judging whether to correct the initial scheduling strategy according to all the fatigue evaluation values; Among them, when establishing multiple wind turbine points, including: Generate fan point number sequence A, A=(a1,a2…a i …a n ), where a i is the i-th wind turbine point, and n is the number of wind turbine points.

2. The wind farm dispatching method based on fatigue distribution according to claim 1, characterized in that: When generating the initial scheduling strategy, including: Establish the expected power series P in the current regulation cycle, P=(p1,p2…p i …p n ), where pi is the expected power of the i-th wind turbine point in the current regulation cycle; Generating the required power b' according to the grid dispatching instruction; Generating the primary power of each wind turbine point within the current adjustment cycle according to the expected power sequence P and the required power b'; Establish a first-level power series B, B=(b1, b2…b i …b n ),b i is the primary power of the i-th fan point in the current regulation cycle; Among them, b i =b'*[p i / ( p i )]; Generating the initial scheduling strategy of the current adjustment cycle according to the primary power sequence B.

3. The wind farm dispatching method based on fatigue distribution according to claim 2, characterized in that: When judging whether to correct the initial scheduling strategy according to all the fatigue evaluation values, including: Setting multiple feedback time nodes within the current adjustment cycle; Obtaining the fatigue evaluation value of each wind turbine point at the current feedback time node; Establish fatigue evaluation value series C, C=(c1,c2…c i …c n ), where c i is the fatigue evaluation value of the i-th wind turbine at the current feedback time node; Generating the correction evaluation value g of the current feedback time node according to the fatigue evaluation value sequence C; Judging whether to generate a correction instruction according to the correction evaluation value g.

4. The wind farm dispatching method based on fatigue distribution according to claim 3, characterized in that: When generating the correction evaluation value g of the current feedback time node, including: g=e1*Q1* (c i -c') 2 ]+e2*Q2* (c i -∆c) 2 ]+e3*Q3*U; U= Y(i)*(c i -c'); Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; c' is the expected fatigue evaluation value of the current feedback time node; ∆c is the average value of all data in the fatigue evaluation value series C; Y(i) is the selection coefficient, if (c i -c')>0,Y(i)=1 / (c i -c'); if (c i -c')<0,Y(i)=0.

5. The wind farm dispatching method based on fatigue distribution according to claim 4, characterized in that: When judging whether to generate a correction instruction according to the correction evaluation value g, including: Presetting the first correction evaluation value threshold G1 and the second correction evaluation value threshold G2; If g < G1, no correction instruction is generated; If G1 ≤ g < G2, generating a primary correction instruction, and generating the power compensation coefficient of each wind turbine point according to the primary correction instruction; Correcting the initial scheduling strategy according to all the power compensation coefficients; If g ≥ G2, generating a secondary correction instruction, and correcting the initial scheduling strategy according to the secondary correction instruction.

6. The wind farm dispatching method based on fatigue distribution according to claim 5, characterized in that: When correcting the initial scheduling strategy according to the secondary correction instruction, including: Generating the fatigue evaluation value threshold C1 of the current feedback time node; If c i >C1, then the i-th fan point is eliminated and the i-th fan point is set as the fan point to be repaired; Generating a maintenance plan according to all the wind turbine points to be overhauled; Establishing the primary wind turbine point sequence A1 of the current feedback time node according to the elimination result; A1=(a 11 ,a 12 …a 1i …a 1n1 ), where is the i-th fan point not eliminated at the current feedback time node; n1 is the number of fan points not eliminated at the current feedback time node, and n1≤n; Generating the distribution coefficient of each primary wind turbine point according to the expected power and the fatigue evaluation value of each primary wind turbine point; Generating the primary scheduling strategy of the current feedback time node according to the distribution coefficient and the required power b'.

7. A wind farm dispatching system based on fatigue distribution, adopting the wind farm dispatching method based on fatigue distribution as described in any one of claims 1 to 6, characterized in that: Including: A central control unit for establishing multiple wind turbine points according to the wind turbine parameters; A monitoring unit including multiple monitoring sub-modules, the monitoring sub-modules are arranged at the wind turbine points for collecting the operation parameters of each wind turbine point; The central control unit includes: A first processing module for establishing multiple adjustment cycles according to the historical environmental parameters, and the first processing module is also used for generating the expected power of each wind turbine point within the current adjustment cycle according to the preset power prediction model; A second processing module for generating an initial scheduling strategy according to the grid dispatching instruction within the current adjustment cycle and the expected power of all wind turbine points; A third processing module for obtaining the fatigue evaluation value of each wind turbine point according to the preset feedback time node; A correction module for judging whether to correct the initial scheduling strategy according to all the fatigue evaluation values; The fourth processing module is used to generate a fan point number sequence A, A=(a1, a2…a i …a n ), where a i is the i-th fan point, n is the number of fan points; The second processing module is also used for: Establish the expected power series P in the current regulation cycle, P=(p1,p2…p i …p n ), where pi is the expected power of the i-th wind turbine point in the current regulation cycle; Generating the required power b' according to the grid dispatching instruction; Generate the primary power of each wind turbine point in the current adjustment period according to the expected power sequence P and the demand power b'. Establish a first-level power series B, B=(b1, b2…b i …b n ),b i is the primary power of the i-th fan point in the current regulation cycle; Among them, b i =b'*[p i / ( p i )]; Generate the initial scheduling strategy for the current adjustment period according to the primary power sequence B.

8. The wind farm dispatching system based on fatigue distribution according to claim 7, characterized in that: The third processing module is further configured to:[[]] Set multiple feedback time nodes within the current adjustment period; Obtain the fatigue evaluation values of each wind turbine point at the current feedback time node; Establish fatigue evaluation value series C, C=(c1,c2…c i …c n ), where c i is the fatigue evaluation value of the i-th wind turbine at the current feedback time node.

9. The wind farm dispatching system based on fatigue distribution according to claim 8, characterized in that: The correction module is further configured to:[[]] Generate a correction evaluation value g for the current feedback time node according to the fatigue evaluation value sequence C; Judge whether to generate a correction instruction according to the correction evaluation value g; g=e1*Q1* (c i -c') 2 ]+e2*Q2* (c i -∆c) 2 ]+e3*Q3*U; U= Y(i)*(c i -c'); Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; c' is the expected fatigue evaluation value of the current feedback time node; ∆c is the average value of all data in the fatigue evaluation value series C; Y(i) is the selection coefficient, if (c i -c')>0,Y(i)=1 / (c i -c'); if (c i -c')<0,Y(i)=0; Judge whether to generate a correction instruction according to the correction evaluation value g.

10. The wind farm dispatching system based on fatigue distribution according to claim 9, characterized in that: When judging whether to generate a correction instruction according to the correction evaluation value g, it includes:[[]] Preset a first correction evaluation value threshold G1 and a second correction evaluation value threshold G2; If g < G1, no correction instruction is generated; If G1 ≤ g < G2, generate a primary correction instruction, and generate a power compensation coefficient for each wind turbine point according to the primary correction instruction; Correct the initial scheduling strategy according to all the power compensation coefficients; If g ≥ G2, generate a secondary correction instruction, and correct the initial scheduling strategy according to the secondary correction instruction.

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